Freight Volume Combined Prediction of Changjiang River Main Line Based on Improved Grey Model

被引：0
|
作者：
Quan, Wen [1 ]
Lei, Li [1 ]
机构：
[1] Changjiang Waterway Inst Planning & Design, Wuhan, Hubei, Peoples R China
来源：
SIXTH INTERNATIONAL CONFERENCE ON ELECTROMECHANICAL CONTROL TECHNOLOGY AND TRANSPORTATION (ICECTT 2021) | 2022年 / 12081卷
关键词：
GM(1,1) model; Quadratic exponential smoothing; Residual correction; Trend extrapolation; Matlab;
D O I：
10.1117/12.2623845
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Based on the freight volume data of the Changjiang River main line from 2005 to 2020, the grey system GM(1,1) model, the quadratic exponential smoothing GM(1,1) model, and the trend extrapolation method are proposed to predict freight volume of the Changjiang River main line. According to those methods, the historical data are calculated by Matlab software and those results are compared and analyzed. At last, the residual periodic correction method is used to correct the predicted results of models, and the final predicted results from 2021 to 2025 are obtained by the combined weighting of previous selected models' predicted results.
引用
收藏
页数：9
相关论文
共 50 条
  • [31] The Prediction of Perishable Products' Sale Volume and Profit in Chongqing Based on Grey Model
    Tang, Yingjia
    Wang, Xu
    Li, LongXiao
    RECENT ADVANCES IN INTELLIGENT MANUFACTURING, PT I, 2018, 923 : 191 - 197
  • [32] The Application of Combination Prediction to Freight Volume of The Jingjiang River Channel Regulation Project
    Liu, Ting
    MATERIALS SCIENCE, CIVIL ENGINEERING AND ARCHITECTURE SCIENCE, MECHANICAL ENGINEERING AND MANUFACTURING TECHNOLOGY, PTS 1 AND 2, 2014, 488-489 : 1391 - 1394
  • [33] Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network
    Guo, Zhi-Da
    Fu, Jing-Yuan
    JOURNAL OF INTELLIGENT SYSTEMS, 2018, 27 (02) : 291 - 302
  • [34] Prediction Method of Railway Freight Volume Based on Genetic Algorithm Improved General Regression Neural Network
    Guo, Zhi-da
    Fu, Jing-Yuan
    JOURNAL OF INTELLIGENT SYSTEMS, 2019, 28 (05) : 835 - 848
  • [35] A novel traffic flow prediction model: Variable order fractional grey model based on an improved grey evolution algorithm
    Zhang, Ruyue
    Mao, Shuhua
    Kang, Yuxiao
    EXPERT SYSTEMS WITH APPLICATIONS, 2023, 224
  • [36] Network Security Situation Prediction Based on Improved Adaptive Grey Verhulst Model
    胡威
    李建华
    陈秀真
    蒋兴浩
    JournalofShanghaiJiaotongUniversity(Science), 2010, 15 (04) : 408 - 413
  • [37] Network security situation prediction based on improved adaptive grey verhulst model
    Hu W.
    Li J.-H.
    Chen X.-Z.
    Jiang X.-H.
    Journal of Shanghai Jiaotong University (Science), 2010, 15 (4) : 408 - 413
  • [38] Prediction for Substation Equipment Failure Rate Based on Improved Grey Combination Model
    Wu G.
    Ni X.
    Song Z.
    Gao B.
    Ni, Xuesong (twocargo@126.com), 1600, Science Press (43): : 2249 - 2255
  • [39] Prediction of spontaneous combustion in the coal stockpile based on an improved metabolic grey model
    Li, Shoujun
    Ma, Xiaoping
    Yang, Chunyu
    PROCESS SAFETY AND ENVIRONMENTAL PROTECTION, 2018, 116 : 564 - 577
  • [40] An Improved Grey Prediction Model Based on Matrix Representations of the Optimized Initial Value
    Wang, Junjie
    Dang, Yaoguo
    Ye, Jing
    Xu, Ning
    Wang, Jiang
    JOURNAL OF GREY SYSTEM, 2018, 30 (03): : 143 - 156
12345